Back

Gender as a Social Determinant of Menstrual Health: A Mixed Methods Study Among Indian Adolescent Girls and Boys

Gundi, M.; Subramanyam, M. A.

2020-08-04 sexual and reproductive health
10.1101/2020.08.04.20167924 medRxiv
Show abstract

Gender bias in the patriarchal Indian society becomes evident in the form of worse sexual and reproductive health outcomes for girls than boys. While girls face menstrual taboos that affect their health, boys understanding of, and participation in, the menstruation discourse remains limited. We investigate how gender through its micro-interactional and macro-structural ways makes menstruation a gendered experience for adolescents; how various social determinants influence girls gendered menstruation experience across social domains; and whether the lived gendered experience of menstruation harms girls health. Using a sequential mixed-methods design semi-structured interviews of 21 boys and girls each; 12 adult key-respondent interviews; and a cross-sectional survey of 1421 adolescents from urban, rural and tribal settings of Nashik district, India, were conducted. Applying social constructivist theory and gender analysis framework, we thematically analysed the qualitative data. Multivariable regression analysis of survey data yielded risk ratios. Adolescents experience of menstruation was gendered. Fewer boys (versus girls) reported receiving information in schools [Incidence Rate Ratio (IRR) at 95% CI: 0.34 (0.24, 0.49)]. Girls gendered menstrual experiences varied across social domains and various socioeconomic backgrounds. Girls menstrual health was poorer among those with a lived experience of gendered menstruation [IRR: 0.22 (0.05, 0.90)]. Key respondents shared the need to engage boys in the menstruation discourse though apprehensive regarding its consequences. Gender bias along with other social factors negatively influence social construction regarding menstruation. Further, the discrimination is embodied by girls as poor health, thus perpetuating health inequalities across socioeconomic settings.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.